Provide independent oversight and challenge throughout the model lifecycle, including development, validation, approval and ongoing monitoring.
Assess model materiality, criticality and alignment with organisational risk appetite.
Review model documentation, assumptions, methodology, limitations, residual risks and compensating controls.
Evaluate model monitoring frameworks, including drift detection, performance and stability metrics.
Ensure compliance with regulatory expectations for AI/ML, including fairness, explainability and accountability.
Collaborate with data scientists and model developers across departments to understand modelling intent and technical assumptions.
Support enhancement of governance frameworks, policies and approval processes for statistical, ML and AI models.
Contribute to AI/ML proof‑of‑concept (POC) initiatives to strengthen governance practices and support innovation.
Partner with risk, compliance, IT and business teams to embed robust AI governance and Responsible AI principles across the organisation.
Support AI/ML model governance by managing essential data assets, including maintaining metadata, documenting key datasets, and ensuring clarity of features and data inputs used in models.
Support data management initiatives for building a robust AI/ML-ready ecosystem.
Requirements
Minimum 7 years of relevant experience in model risk management, model governance, model validation, quantitative analytics or related areas.
Strong knowledge of model governance frameworks, policies and regulatory expectations for statistical, ML and AI models.
Understanding of AI/ML concepts including performance evaluation, explainability, drift and monitoring techniques.
Ability to identify modelling weaknesses, design flaws, performance gaps and potential risks.
Experience reviewing documentation, assumptions, model logic and validation evidence.
Strong risk assessment, judgement, analytical and problem‑solving skills.
Excellent documentation and communication skills to support governance decisions.
Ability to collaborate effectively with data science, engineering, business and risk stakeholders.
Familiarity with Responsible AI principles such as fairness, transparency and robustness.
Nice-to-haves: AI Governance Professional (AIGP) certificate or relevant qualifications.
